Enterprise AI Analysis
Artificial intelligence in otorhinolaryngology: current trends and application areas
This study aims to perform a bibliometric analysis of scientific research on the use of artificial intelligence (AI) in the field of Otorhinolaryngology (ORL), with a specific focus on identifying emerging AI trend topics within this discipline. Methods A total of 498 articles on AI in ORL, published between 1982 and 2024, were retrieved from the Web of Science database. Various bibliometric techniques, including trend keyword analysis and factor analysis, were applied to analyze the data. Results The most prolific journal was the European Archives of Oto-Rhino-Laryngology (n=67). The USA (n=200) and China (n=61) were the most productive countries in AI-related ORL research. The most productive institutions were Harvard University / Harvard Medical School (n=71). The leading authors in this field were Lechien JR. (n=18) and Rameau A. (n=17). The most frequently used keywords in the AI research were cochlear implant, head and neck cancer, magnetic resonance imaging (MRI), hearing loss, patient education, diagnosis, radiomics, surgery, hearing aids, laryngology ve otitis media. Recent trends in otorhinolaryngology research reflect a dynamic focus, progressing from hearing-related technologies such as hearing aids and cochlear implants in earlier years, to diagnostic innovations like audiometry, psychoacoustics, and narrow band imaging. The emphasis has recently shifted toward advanced applications of MRI, radiomics, and computed tomography (CT) for conditions such as head and neck cancer, chronic rhinosinusitis, laryngology, and otitis media. Additionally, increasing attention has been given to patient education, quality of life, and prognosis, underscoring a holistic approach to diagnosis, surgery, and treatment in otorhinolaryngology. Conclusion Al has significantly impacted the field of ORL, especially in diagnostic imaging and therapeutic planning. With advancements in MRI and CT-based technologies, AI has proven to enhance disease detection and management. The future of AI in ORL suggests a promising path toward improving clinical decision-making, patient care, and healthcare efficiency.
Executive Impact Summary
This bibliometric analysis reveals a rapid surge in Artificial Intelligence (AI) research within Otorhinolaryngology (ORL), particularly since 2018. AI is profoundly impacting diagnostic imaging, treatment planning, and patient education. Key areas of application include cochlear implants, head and neck cancer, and advanced MRI/CT radiomics. The research landscape is dominated by the USA and Harvard University, with the European Archives of Oto-Rhino-Laryngology being the most prolific journal. The evolution of AI trends in ORL reflects a shift from hearing-related technologies to sophisticated diagnostic imaging and holistic patient management, enhancing accuracy, efficiency, and personalized patient care.
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Deep Analysis & Enterprise Applications
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Summarizes the foundational quantitative analysis of AI research in ORL.
2018
AI publications began rapid increase starting in
| Category | Top Contributors |
|---|---|
| Countries | USA (n=200), China (n=61), Germany (n=25), Japan (n=23), UK (n=23) |
| Institutions | Harvard University (42), Harvard Medical School (29), University of Toronto (26), Weill Cornell Medicine (26), Massachusetts Eye and Ear Infirmary (25) |
European Archives of Oto-Rhino-Laryngology
Most Prolific Journal (n=67)
Explores the temporal evolution of AI application areas within ORL research.
| Application Type | Examples |
|---|---|
| Hearing-Related | Cochlear Implants, Hearing Aids, Audiometry, Psychoacoustics |
| Oncology | Head and Neck Cancer, Laryngeal Cancer, Nasopharyngeal Carcinoma |
| Imaging & Diagnosis | MRI, CT, Radiomics, Computer Vision, Otoscopy, Endoscopy |
| Patient Management | Patient Education, Prognosis, Quality of Life, Decision Support Systems |
Key Topic Evolution (2018-2024)
2018: Hearing Aids
→
2019: Quality of Life
→
2020: Audiometry, Psychoacoustics, Audiogram
→
2021: Human Papillomavirus, Hearing Loss, Cochlear Implant
→
2022: Narrow Band Imaging, Voice Disorders, Vestibular Schwannoma, Otoscopy, Acoustic Neuroma, Oropharyngeal Cancer
→
2023: Prognosis, CT, Radiomics, Otitis Media, MRI, Head and Neck Cancer
→
2024: Chronic Rhinosinusitis, Computer Vision, Laryngology, Surgery, Diagnosis, Patient Education
AI Enhances Patient Education
Problem: Patients often face inaccurate or complex medical information from general sources, leading to anxiety or inappropriate treatments.
Solution: AI-powered chatbots (like ChatGPT) simplify medical terminology, answer questions, and provide personalized insights. For example, in otolaryngology, AI has made information on anosmia, tracheotomy, and laryngopharyngeal reflux more accessible.
Impact: Improved patient understanding, better compliance with treatment plans, and enhanced quality of healthcare delivery.
Delves into the underlying conceptual clusters derived from factor analysis of AI literature in ORL.
| Cluster | Key Focus Areas |
|---|---|
| Comprehensive ORL Applications | Cochlear Implants, Head and Neck Cancer, Hearing Loss, Patient Education (Diagnosis, Treatment, Management of various ORL pathologies) |
| Visual Data Analysis (MRI-based) | Radiomics, Chronic Rhinosinusitis, Acoustic Neuroma, Vestibular Schwannoma (Diagnostic accuracy, Surgical planning) |
| CT Imaging Applications | Oropharyngeal Cancer, Skull Base Pathologies, Paranasal Sinus Diseases (Imaging-based diagnosis, Treatment strategies) |
| Hearing-Related Studies | Audiograms, Audiometry, Psychoacoustics |
Radiologists, Surgeons, Data Scientists
Key Disciplines for AI Integration
AI Revolutionizes Diagnostic Imaging
Problem: Complex anatomical structures in the head and neck region make MRI analysis time-consuming and prone to expert dependency for tumor detection.
Solution: AI, through radiomics and deep learning, automatically analyzes MRI data to predict tumor size, malignancy risk, lymph node metastasis, bone invasion, and postoperative complications. It also differentiates eosinophilic from non-eosinophilic chronic rhinosinusitis on CT images and detects anatomical landmarks in temporal bone CT scans.
Impact: Enhanced diagnostic accuracy, personalized treatment planning, accelerated clinical decision-making, and improved surgical planning.
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Your AI Implementation Roadmap
A typical journey to integrate AI into your enterprise, tailored to the insights from this analysis.
Phase 1: Discovery & Strategy
Initial consultation, assessment of current workflows, identification of high-impact AI opportunities in ORL diagnostics and patient management. Define project scope, KPIs, and success metrics.
Phase 2: Data Preparation & Model Development
Curate and preprocess ORL-specific datasets (e.g., MRI/CT images, audiogram data, patient records). Develop and train custom AI models for diagnosis, prognosis, and patient education based on identified trends.
Phase 3: Integration & Testing
Integrate AI solutions into existing clinical workflows and IT infrastructure. Rigorous testing for accuracy, reliability, and ethical compliance within ORL practice. User acceptance testing with specialists.
Phase 4: Deployment & Optimization
Full-scale deployment of AI tools for enhanced diagnostic imaging, personalized treatment planning, and patient education. Continuous monitoring, feedback, and iterative optimization for peak performance and user satisfaction.
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